---
title: Taiga
---

This guide provides a quick overview for getting started with Taiga tooling in [langchain_taiga](https://github.com/Shikenso-Analytics/langchain-taiga/blob/main/docs/tools.ipynb). For more details on each tool and configuration, see the docstrings in your repository or relevant doc pages.

## Overview

### Integration details

| Class                                                                                                | Package                                                                    | Serializable | JS support |                                        Version                                        |
|:-----------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------| :---:        |:------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------:|
| `create_entity_tool`, `search_entities_tool`, `get_entity_by_ref_tool`, `update_entity_by_ref_tool` , `add_comment_by_ref_tool`, `add_attachment_by_ref_tool` | [langchain-taiga](https://github.com/Shikenso-Analytics/langchain-taiga)   | N/A          |                                      TBD                                       | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-taiga?style=flat-square&label=%20) |

### Tool features

- **`create_entity_tool`**: Creates user stories, tasks and issues in Taiga.
- **`search_entities_tool`**: Searches for user stories, tasks and issues in Taiga.
- **`get_entity_by_ref_tool`**: Gets a user story, task or issue by reference.
- **`update_entity_by_ref_tool`**: Updates a user story, task or issue by reference.
- **`add_comment_by_ref_tool`**: Adds a comment to a user story, task or issue.
- **`add_attachment_by_ref_tool`**: Adds an attachment to a user story, task or issue.

## Setup

The integration lives in the `langchain-taiga` package.

```python
%pip install --quiet -U langchain-taiga
```

```output
/home/henlein/Workspace/PyCharm/langchain/.venv/bin/python: No module named pip
Note: you may need to restart the kernel to use updated packages.
```

### Credentials

This integration requires you to set `TAIGA_URL`, `TAIGA_API_URL`, `TAIGA_USERNAME`, `TAIGA_PASSWORD` and `OPENAI_API_KEY` as environment variables to authenticate with Taiga.

```bash
export TAIGA_URL="https://taiga.xyz.org/"
export TAIGA_API_URL="https://taiga.xyz.org/"
export TAIGA_USERNAME="username"
export TAIGA_PASSWORD="pw"
export OPENAI_API_KEY="OPENAI_API_KEY"
```

It's also helpful (but not needed) to set up [LangSmith](https://smith.langchain.com/) for best-in-class observability:

```python
# os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass()
```

## Instantiation

Below is an example showing how to instantiate the Taiga tools in `langchain_taiga`. Adjust as needed for your specific usage.

```python
from langchain_taiga.tools.discord_read_messages import create_entity_tool
from langchain_taiga.tools.discord_send_messages import search_entities_tool

create_tool = create_entity_tool
search_tool = search_entities_tool
```

## Invocation

### Direct invocation with args

Below is a simple example of calling the tool with keyword arguments in a dictionary.

```python
from langchain_taiga.tools.taiga_tools import (
    add_attachment_by_ref_tool,
    add_comment_by_ref_tool,
    create_entity_tool,
    get_entity_by_ref_tool,
    search_entities_tool,
    update_entity_by_ref_tool,
)

response = create_entity_tool.invoke(
    {
        "project_slug": "slug",
        "entity_type": "us",
        "subject": "subject",
        "status": "new",
        "description": "desc",
        "parent_ref": 5,
        "assign_to": "user",
        "due_date": "2022-01-01",
        "tags": ["tag1", "tag2"],
    }
)

response = search_entities_tool.invoke(
    {"project_slug": "slug", "query": "query", "entity_type": "task"}
)

response = get_entity_by_ref_tool.invoke(
    {"entity_type": "user_story", "project_id": 1, "ref": "1"}
)

response = update_entity_by_ref_tool.invoke(
    {"project_slug": "slug", "entity_ref": 555, "entity_type": "us"}
)


response = add_comment_by_ref_tool.invoke(
    {"project_slug": "slug", "entity_ref": 3, "entity_type": "us", "comment": "new"}
)

response = add_attachment_by_ref_tool.invoke(
    {
        "project_slug": "slug",
        "entity_ref": 3,
        "entity_type": "us",
        "attachment_url": "url",
        "content_type": "png",
        "description": "desc",
    }
)
```

### Invocation with ToolCall

If you have a model-generated `ToolCall`, pass it to `tool.invoke()` in the format shown below.

```python
# This is usually generated by a model, but we'll create a tool call directly for demo purposes.
model_generated_tool_call = {
    "args": {"project_slug": "slug", "query": "query", "entity_type": "task"},
    "id": "1",
    "name": search_entities_tool.name,
    "type": "tool_call",
}
tool.invoke(model_generated_tool_call)
```

## Chaining

Below is a more complete example showing how you might integrate the `create_entity_tool` and `search_entities_tool` tools in a chain or agent with an LLM. This example assumes you have a function (like `create_agent`) that sets up a LangChain-style agent capable of calling tools when appropriate.

```python
# Example: Using Taiga Tools in an Agent

from langchain.agents import create_agent
from langchain_taiga.tools.taiga_tools import create_entity_tool, search_entities_tool

# 1. Instantiate or configure your language model
# (Replace with your actual LLM, e.g., ChatOpenAI(temperature=0))
llm = ...

# 2. Build an agent that has access to these tools
agent_executor = create_agent(llm, [create_entity_tool, search_entities_tool])

# 4. Formulate a user query that may invoke one or both tools
example_query = "Please create a new user story with the subject 'subject' in slug project: 'slug'"

# 5. Execute the agent in streaming mode (or however your code is structured)
events = agent_executor.stream(
    {"messages": [("user", example_query)]},
    stream_mode="values",
)

# 6. Print out the model's responses (and any tool outputs) as they arrive
for event in events:
    event["messages"][-1].pretty_print()
```

## API reference

See the docstrings in:

- [taiga_tools.py](https://github.com/Shikenso-Analytics/langchain-taiga/blob/main/langchain_taiga/tools/taiga_tools.py)
- [toolkits.py](https://github.com/Shikenso-Analytics/langchain-taiga/blob/main/langchain_taiga/toolkits.py)

for usage details, parameters, and advanced configurations.
